A Deep Intronic PKHD1 Variant Identified by SpliceAI in a Deceased Neonate With Autosomal Recessive Polycystic Kidney Disease.

The etiologies of newborn deaths in neonatal intensive care units usually remain unknown, even after genetic testing. Whole-genome sequencing, combined with artificial intelligence-based methods for predicting the effects of non-coding variants, provide an avenue for resolving these deaths. Using one such method, SpliceAI, we identified a maternally inherited deep intronic PKHD1 splice variant (chr6:52030169T>C), in trans with a pathogenic missense variant (p.Thr36Met), in a newborn who died of autosomal recessive polycystic kidney disease at age 2 days. We validated the deep intronic variant's impact in maternal urine-derived cells expressing PKHD1. Reverse transcription polymerase chain reaction followed by Sanger sequencing showed that the variant causes inclusion of 147bp of the canonical intron between exons 29 and 30 of PKHD1 into the mRNA, including a premature stop codon. Allele-specific expression analysis at a heterozygous site in the mother showed that the mutant allele completely suppresses canonical splicing. In an unrelated healthy control, there was no evidence of transcripts including the novel splice junction. We returned a diagnostic report to the parents, who underwent in vitro embryo selection.

Contribution of mutant HSC clones to immature and mature cells in MDS and CMML, and variations with AZA therapy.

Myelodysplastic neoplasms (MDSs) and chronic myelomonocytic leukemia (CMML) are clonal disorders driven by progressively acquired somatic mutations in hematopoietic stem cells (HSCs). Hypomethylating agents (HMAs) can modify the clinical course of MDS and CMML. Clinical improvement does not require eradication of mutated cells and may be related to improved differentiation capacity of mutated HSCs. However, in patients with established disease it is unclear whether (1) HSCs with multiple mutations progress through differentiation with comparable frequency to their less mutated counterparts or (2) improvements in peripheral blood counts following HMA therapy are driven by residual wild-type HSCs or by clones with particular combinations of mutations. To address these questions, the somatic mutations of individual stem cells, progenitors (common myeloid progenitors, granulocyte monocyte progenitors, and megakaryocyte erythroid progenitors), and matched circulating hematopoietic cells (monocytes, neutrophils, and naïve B cells) in MDS and CMML were characterized via high-throughput single-cell genotyping, followed by bulk analysis in immature and mature cells before and after AZA treatment. The mutational burden was similar throughout differentiation, with even the most mutated stem and progenitor clones maintaining their capacity to differentiate to mature cell types in vivo. Increased contributions from productive mutant progenitors appear to underlie improved hematopoiesis in MDS following HMA therapy.

Bone Marrow Surveillance of Pediatric Cancer Survivors Identifies Clones that Predict Therapy-Related Leukemia.

PURPOSE: Therapy-related myelodysplastic syndrome and acute leukemias (t-MDS/AL) are a major cause of nonrelapse mortality among pediatric cancer survivors. Although the presence of clonal hematopoiesis (CH) in adult patients at cancer diagnosis has been implicated in t-MDS/AL, there is limited published literature describing t-MDS/AL development in children. EXPERIMENTAL DESIGN: We performed molecular characterization of 199 serial bone marrow samples from 52 patients treated for high-risk neuroblastoma, including 17 with t-MDS/AL (transformation), 14 with transient cytogenetic abnormalities (transient), and 21 without t-MDS/AL or cytogenetic alterations (neuroblastoma-treated control). We also evaluated for CH in a cohort of 657 pediatric patients with solid tumor. RESULTS: We detected at least one disease-defining alteration in all cases at t-MDS/AL diagnosis, most commonly TP53 mutations and KMT2A rearrangements, including involving two novel partner genes (PRDM10 and DDX6). Backtracking studies identified at least one t-MDS/AL-associated mutation in 13 of 17 patients at a median of 15 months before t-MDS/AL diagnosis (range, 1.3-32.4). In comparison, acquired mutations were infrequent in the transient and control groups (4/14 and 1/21, respectively). The relative risk for development of t-MDS/AL in the presence of an oncogenic mutation was 8.8 for transformation patients compared with transient. Unlike CH in adult oncology patients, TP53 mutations were only detectable after initiation of cancer therapy. Last, only 1% of pediatric patients with solid tumor evaluated had CH involving myeloid genes. CONCLUSIONS: These findings demonstrate the clinical relevance of identifying molecular abnormalities in predicting development of t-MDS/AL and should guide the formation of intervention protocols to prevent this complication in high-risk pediatric patients.

Viral genome sequence datasets display pervasive evidence of strand-specific substitution biases that are best described using non-reversible nucleotide substitution models.

Background: The vast majority of phylogenetic trees are inferred from molecular sequence data (nucleotides or amino acids) using time-reversible evolutionary models which assume that, for any pair of nucleotide or amino acid characters, the relative rate of X to Y substitution is the same as the relative rate of Y to X substitution. However, this reversibility assumption is unlikely to accurately reflect the actual underlying biochemical and/or evolutionary processes that lead to the fixation of substitutions. Here, we use empirical viral genome sequence data to reveal that evolutionary non-reversibility is pervasive among most groups of viruses. Specifically, we consider two non-reversible nucleotide substitution models: (1) a 6-rate non-reversible model (NREV6) in which Watson-Crick complementary substitutions occur at identical relative rates and which might therefor be most applicable to analyzing the evolution of genomes where both complementary strands are subject to the same mutational processes (such as might be expected for double-stranded (ds) RNA or dsDNA genomes); and (2) a 12-rate non-reversible model (NREV12) in which all relative substitution types are free to occur at different rates and which might therefore be applicable to analyzing the evolution of genomes where the complementary genome strands are subject to different mutational processes (such as might be expected for viruses with single-stranded (ss) RNA or ssDNA genomes). Results: Using likelihood ratio and Akaike Information Criterion-based model tests, we show that, surprisingly, NREV12 provided a significantly better fit to 21/31 dsRNA and 20/30 dsDNA datasets than did the general time reversible (GTR) and NREV6 models with NREV6 providing a better fit than NREV12 and GTR in only 5/30 dsDNA and 2/31 dsRNA datasets. As expected, NREV12 provided a significantly better fit to 24/33 ssDNA and 40/47 ssRNA datasets. Next, we used simulations to show that increasing degrees of strand-specific substitution bias decrease the accuracy of phylogenetic inference irrespective of whether GTR or NREV12 is used to describe mutational processes. However, in cases where strand-specific substitution biases are extreme (such as in SARS-CoV-2 and Torque teno sus virus datasets) NREV12 tends to yield more accurate phylogenetic trees than those obtained using GTR. Conclusion: We show that NREV12 should, be seriously considered during the model selection phase of phylogenetic analyses involving viral genomic sequences.

Th17 reprogramming of T cells in systemic juvenile idiopathic arthritis.

Systemic juvenile idiopathic arthritis (sJIA) begins with fever, rash, and high-grade systemic inflammation but commonly progresses to a persistent afebrile arthritis. The basis for this transition is unknown. To evaluate a role for lymphocyte polarization, we characterized T cells from patients with acute and chronic sJIA using flow cytometry, mass cytometry, and RNA sequencing. Acute and chronic sJIA each featured an expanded population of activated Tregs uncommon in healthy controls or in children with nonsystemic JIA. In acute sJIA, Tregs expressed IL-17A and a gene expression signature reflecting Th17 polarization. In chronic sJIA, the Th17 transcriptional signature was identified in T effector cells (Teffs), although expression of IL-17A at the protein level remained rare. Th17 polarization was abrogated in patients responding to IL-1 blockade. These findings identify evolving Th17 polarization in sJIA that begins in Tregs and progresses to Teffs, likely reflecting the impact of the cytokine milieu and consistent with a biphasic model of disease pathogenesis. The results support T cells as a potential treatment target in sJIA.

Chimeric Antigen Receptor T Cells Targeting NKG2D-Ligands Show Robust Efficacy Against Acute Myeloid Leukemia and T-Cell Acute Lymphoblastic Leukemia.

CAR T cell approaches to effectively target AML and T-ALL without off-tumor effects on healthy myeloid or T cell compartments respectively are an unmet medical need. NKG2D-ligands are a promising target given their absence on healthy cells and surface expression in a wide range of malignancies. NKG2D-ligand expression has been reported in a substantial group of patients with AML along with evidence for prognostic significance. However, reports regarding the prevalence and density of NKG2D-ligand expression in AML vary and detailed studies to define whether low level expression is sufficient to trigger NKG2D-ligand directed CART cell responses are lacking. NKG2D ligand expression in T-ALL has not previously been interrogated. Here we report that NKG2D-ligands are expressed in T-ALL cell lines and primary T-ALL. We confirm that NKG2D-ligands are frequently surface expressed in primary AML, albeit at relatively low levels. Utilizing CAR T cells incorporating the natural immune receptor NKG2D as the antigen binding domain, we demonstrate striking in vitro activity of CAR T cells targeting NKG2D-ligands against AML and T-ALL cell lines and show that even low-level ligand expression in primary AML targets results in robust NKG2D-CAR activity. We found that NKG2D-ligand expression can be selectively enhanced in low-expressing AML cell lines and primary AML blasts via pharmacologic HDAC inhibition. Such pharmacologic NKG2D-ligand induction results in enhanced NKG2D-CAR anti-leukemic activity without affecting healthy PBMC, thereby providing rationale for the combination of HDAC-inhibitors with NKG2D-CAR T cell therapy as a potential strategy to achieve clinical NKG2D-CAR T cell efficacy in AML.

A systems-level analysis of drug-target-disease associations for drug repositioning.

Drug repositioning is the process of finding new therapeutic uses for existing, approved drugs-a process thathas value when considering the exorbitant costs of novel drug development. Several computational strategies exist as a way to predict these alternative applications. In this study, we used datasets on: (1) human biological drug targets and (2) disease-associated genes and, based on a direct functional interaction between them, searched for potential opportunities for drug repositioning. From the set of 1125 unique drug targets and their 88 490 interactions with disease-associated genes, 30 drug targets were analyzed and (3) discussed in detail for the purpose of this article. The current indications of the drugs thattarget them were validated through the interactions, and new opportunities for repositioning were predicted. Among the set of drugs for potential repositioning werebenzodiazepines for the treatment of autism spectrum disorders; nortriptyline for the treatment of melanoma, glioma and other cancers; and vitamin B6 in prevention of spontaneous abortions and cleft palate birth defects. Special emphasis was also placed on those new potential indications that pertained to orphan diseases-these are diseases whose rarity means that development of novel treatment is not financially viable. This computational drug repositioning approach uses existing information on drugs and drug targets, and insights into the genetic basis of disease, as a means to systematically generate the most probable new uses for the drugs on offer, and in this way harness their true therapeutic power.

Large-scale data-driven integrative framework for extracting essential targets and processes from disease-associated gene data sets.

Populations worldwide currently face several public health challenges, including growing prevalence of infections and the emergence of new pathogenic organisms. The cost and risk associated with drug development make the development of new drugs for several diseases, especially orphan or rare diseases, unappealing to the pharmaceutical industry. Proof of drug safety and efficacy is required before market approval, and rigorous testing makes the drug development process slow, expensive and frequently result in failure. This failure is often because of the use of irrelevant targets identified in the early steps of the drug discovery process, suggesting that target identification and validation are cornerstones for the success of drug discovery and development. Here, we present a large-scale data-driven integrative computational framework to extract essential targets and processes from an existing disease-associated data set and enhance target selection by leveraging drug-target-disease association at the systems level. We applied this framework to tuberculosis and Ebola virus diseases combining heterogeneous data from multiple sources, including protein-protein functional interaction, functional annotation and pharmaceutical data sets. Results obtained demonstrate the effectiveness of the pipeline, leading to the extraction of essential drug targets and to the rational use of existing approved drugs. This provides an opportunity to move toward optimal target-based strategies for screening available drugs and for drug discovery. There is potential for this model to bridge the gap in the production of orphan disease therapies, offering a systematic approach to predict new uses for existing drugs, thereby harnessing their full therapeutic potential.